Multimodal tract-based MRI metrics outperform whole brain markers in determining cognitive impact of small vessel disease-related brain injury
De Luca, Alberto; Kuijf, Hugo; Exalto, Lieza; Thiebaut de Schotten, Michel; Biessels, Geert-Jan; Utrecht VCI Study Group
(2022) Brain structure & function, volume 227, issue 7, pp. 2553 - 2567
(Article)
Abstract
In cerebral small vessel disease (cSVD), whole brain MRI markers of cSVD-related brain injury explain limited variance to support individualized prediction. Here, we investigate whether considering abnormalities in brain tracts by integrating multimodal metrics from diffusion MRI (dMRI) and structural MRI (sMRI), can better capture cognitive performance in cSVD patients
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than established approaches based on whole brain markers. We selected 102 patients (73.7 ± 10.2 years old, 59 males) with MRI-visible SVD lesions and both sMRI and dMRI. Conventional linear models using demographics and established whole brain markers were used as benchmark of predicting individual cognitive scores. Multi-modal metrics of 73 major brain tracts were derived from dMRI and sMRI, and used together with established markers as input of a feed-forward artificial neural network (ANN) to predict individual cognitive scores. A feature selection strategy was implemented to reduce the risk of overfitting. Prediction was performed with leave-one-out cross-validation and evaluated with the R 2 of the correlation between measured and predicted cognitive scores. Linear models predicted memory and processing speed with R 2 = 0.26 and R 2 = 0.38, respectively. With ANN, feature selection resulted in 13 tract-specific metrics and 5 whole brain markers for predicting processing speed, and 28 tract-specific metrics and 4 whole brain markers for predicting memory. Leave-one-out ANN prediction with the selected features achieved R 2 = 0.49 and R 2 = 0.40 for processing speed and memory, respectively. Our results show proof-of-concept that combining tract-specific multimodal MRI metrics can improve the prediction of cognitive performance in cSVD by leveraging tract-specific multi-modal metrics.
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Keywords: Cerebral small vessel disease, Cognition, Diffusion MRI, Fiber tractography, Neural network, Anatomy, General Neuroscience, Histology
ISSN: 1863-2653
Publisher: Springer Verlag
Note: Funding Information: This work is supported by the Netherlands CardioVascular Research Initiative: the Dutch Heart Foundation (CVON 2018-28 and 2012-06 Heart Brain Connection). The work of AdL and GJB is also supported by Vici Grant 918.16.616 from ZonMw (NL). Funding Information: This work is supported by the Netherlands CardioVascular Research Initiative: the Dutch Heart Foundation (CVON 2018-28 and 2012-06 Heart Brain Connection). The work of AdL and GJB is also supported by Vici Grant 918.16.616 from ZonMw (NL). Members of the Utrecht Vascular Cognitive Impairment (VCI) Study group involved in the present study (in alphabetical order by department): University Medical Center Utrecht, the Netherlands, Department of Neurology: E. van den Berg, G.J. Biessels, L.G. Exalto, C.J.M. Frijns, O. Groeneveld, R.Heinen, S.M. Heringa, L.J. Kappelle, Y.D. Reijmer, J. Verwer, N. Vlegels; Department of Radiology/Image Sciences Institute: J. de Bresser, A. De Luca, H.J. Kuijf, A. Leemans; Department of Geriatrics: H.L. Koek; Hospital Diakonessenhuis Zeist, the Netherlands: M. Hamaker, R. Faaij, M. Pleizier, E. Vriens. Funding Information: This work is part of the Heart-Brain Connection crossroads (HBCx) consortium of the Dutch CardioVascular Alliance (DCVA). HBCx has received funding from the Dutch Heart Foundation under grant agreement 2018-28. Publisher Copyright: © 2022, The Author(s).
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